Artificial Intelligence in Human Reproduction.

The use of artificial intelligence (AI) in human reproduction is a rapidly evolving field with both exciting possibilities and ethical considerations. This technology has the potential to improve success rates and reduce the emotional and financial burden of infertility. However, it also raises ethical and privacy concerns.

Use of artificial intelligence embryo selection based on static images to predict first-trimester pregnancy loss.

Research Question: Can an artificial intelligence embryo selection assistant predict the incidence of first-trimester spontaneous abortion using static images of IVF embryos?

Design: In a blind, retrospective study, a cohort of 172 blastocysts from IVF cases with single embryo transfer and a positive biochemical pregnancy test was ranked retrospectively by the artificial intelligence morphometric algorithm ERICA. Making use of static embryo images from a light microscope, each blastocyst was assigned to one of four possible groups (optimal, good, fair or poor), and linear regression was used to correlate the results with the presence or absence of a normal fetal heart beat as an indicator of ongoing pregnancy or spontaneous abortion, respectively. Additional analyses included modelling for recipient age and chromosomal status established by preimplantation genetic testing for aneuploidy (PGT-A).

New frontiers in embryo selection.

Human infertility is a major global public health issue estimated to affect one out of six couples, while the number of assisted reproduction cycles grows impressively year over year. Efforts to alleviate infertility using advanced technology are gaining traction rapidly as infertility has an enormous impact on couples and the potential to destabilize entire societies if replacement birthrates are not achieved. Artificial intelligence (AI) technologies, leveraged by the highly advanced assisted reproductive technology (ART) industry, are a promising addition to the armamentarium of tools available to combat global infertility.

Automated identification of blastocyst regions at different development stages.

The selection of the best single blastocyst for transfer is typically based on the assessment of the morphological characteristics of the zona pellucida (ZP), trophectoderm (TE), blastocoel (BC), and inner cell-mass (ICM), using subjective and observer-dependent grading protocols. We propose the first automatic method for segmenting all morphological structures during the different developmental stages of the blastocyst (i.e.

Computer software (SiD) assisted real-time single sperm selection associated with fertilization and blastocyst formation.

Research Question: Is it possible to explore an association between individual sperm kinematics evaluated in real time and spermatozoa selected by an embryologist for intracytoplasmic sperm injection (ICSI), with subsequent normal fertilization and blastocyst formation using a novel artificial vision-based software (SiD V1.0; IVF 2.0, UK)?

Design: ICSI procedures were randomly video recorded and subjected to analysis using SiD V1.

Automation in ART: Paving the Way for the Future of Infertility Treatment.

In vitro fertilisation (IVF) is estimated to account for the birth of more than nine million babies worldwide, perhaps making it one of the most intriguing as well as commoditised and industrialised modern medical interventions. Nevertheless, most IVF procedures are currently limited by accessibility, affordability and most importantly multistep, labour-intensive, technically challenging processes undertaken by skilled professionals. Therefore, in order to sustain the exponential demand for IVF on one hand, and streamline existing processes on the other, innovation is essential.

Predictive modeling in reproductive medicine: Where will the future of artificial intelligence research take us?

Artificial intelligence (AI) systems have been proposed for reproductive medicine since 1997. Although AI is the main driver of emergent technologies in reproduction, such as robotics, Big Data, and internet of things, it will continue to be the engine for technological innovation for the foreseeable future. What does the future of AI research look like?

Evaluating predictive models in reproductive medicine.

Predictive modeling has become a distinct subdiscipline of reproductive medicine, and researchers and clinicians are just learning the skills and expertise to evaluate artificial intelligence (AI) studies. Diagnostic tests and model predictions are subject to evaluation. Their use offers potential for both harm and benefit in terms of diagnosis, treatment, and prognosis.

Embryo Ranking Intelligent Classification Algorithm (ERICA): artificial intelligence clinical assistant predicting embryo ploidy and implantation.

Research Question: Can a deep machine learning artificial intelligence algorithm predict ploidy and implantation in a known data set of static blastocyst images, and how does its performance compare against chance and experienced embryologists?

Design: A database of blastocyst images with known outcome was applied with an algorithm dubbed ERICA (Embryo Ranking Intelligent Classification Algorithm). It was evaluated against its ability to predict euploidy, compare ploidy prediction against randomly assigned prognosis labels and against senior embryologists, and if it could rank an euploid embryo highly.

Results: A total of 1231 embryo images were classed as good prognosis if euploid and implanted or poor prognosis if aneuploid and failed to implant.

Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning.

Assessing the viability of a blastosyst is still empirical and non-reproducible nowadays. We developed an algorithm based on artificial vision and machine learning (and other classifiers) that predicts pregnancy using the beta human chorionic gonadotropin (b-hCG) test from both the morphology of an embryo and the age of the patients. We employed two high-quality databases with known pregnancy outcomes (n = 221).

Live birth derived from oocyte spindle transfer to prevent mitochondrial disease.

Mutations in mitochondrial DNA (mtDNA) are maternally inherited and can cause fatal or debilitating mitochondrial disorders. The severity of clinical symptoms is often associated with the level of mtDNA mutation load or degree of heteroplasmy. Current clinical options to prevent transmission of mtDNA mutations to offspring are limited.

Minimal stimulation IVF vs conventional IVF: a randomized controlled trial.

Background: Minimal stimulation in vitro fertilization (mini-in vitro fertilization) is an alternative in vitro fertilization treatment protocol that may reduce ovarian hyperstimulation syndrome, multiple pregnancy rates, and cost while retaining high live birth rates.

Objective: We performed a randomized noninferiority controlled trial with a prespecified border of 10% that compared 1 cycle of mini-in vitro fertilization with single embryo transfer with 1 cycle of conventional in vitro fertilization with double embryo transfer.

Study Design: Five hundred sixty-four infertile women (<39 years old) who were undergoing their first in vitro fertilization cycle were allocated randomly to either mini-in vitro fertilization or conventional in vitro fertilization.

Adnexal torsion, a medical complication of in vitro fertilization.

With the expanding use of assisted reproductive technologies, general obstetricians and gynaecologists must maintain a high index of suspicion for complications of in vitro fertilization. We present the case of a pregnant woman with unilateral adnexal torsion following in vitro fertilization and its diagnostic implications in an emergency setting.

Necrospermia, antisperm antibodies, and vasectomy.

Objective: To present a case of necrospermia and antisperm antibodies after vasectomy reversal and in which motile sperm, subsequently used in intracytoplasmic sperm injection (ICSI) treatment, was found after testicular sperm retrieval.

Design: Case report and literature review.

Setting: Reproductive medicine unit based in a women's hospital in the United Kingdom.